Neuroscientists find a way to make object-recognition models perform better

Adding a module that mimics part of the brain can prevent common errors made by computer vision models.

Anne Trafton | MIT News Office • mit
Dec. 3, 2020 ~8 min

How humans use objects in novel ways to solve problems

What's SSUP? The Sample, Simulate, Update cognitive model developed by MIT researchers learns to use tools like humans do.

Center for Brains, Minds and Machines • mit
Nov. 24, 2020 ~5 min

Neural pathway crucial to successful rapid object recognition in primates

Recurrent processing via prefrontal cortex, necessary for quick visual object processing in primates, provides a key insight for developing brain-like artificial intelligence.

Alli Gold | School of Science • mit
Oct. 20, 2020 ~5 min

How we make moral decisions

In some situations, asking “what if everyone did that?” is a common strategy for judging whether an action is right or wrong.

Anne Trafton | MIT News Office • mit
Oct. 2, 2020 ~7 min

Study suggests animals think probabilistically to distinguish contexts

New statistical model may help scientists understand how animals infer whether surroundings are novel or haven’t changed enough to be a new context.

David Orenstein | Picower Institute for Learning and Memory • mit
Aug. 12, 2020 ~6 min

Key brain region was “recycled” as humans developed the ability to read

Part of the visual cortex dedicated to recognizing objects appears predisposed to identifying words and letters, a study finds.

Anne Trafton | MIT News Office • mit
Aug. 4, 2020 ~7 min

Looking into the black box

Recent advances give theoretical insight into why deep learning networks are successful.

Sabbi Lall | McGovern Institute for Brain Research • mit
July 27, 2020 ~6 min

Universal musical harmony

Acoustic and biological constraints shape how we hear harmony across cultures.

Sabbi Lall | McGovern Institute for Brain Research • mit
July 1, 2020 ~7 min

A new model of vision

Computer model of face processing could reveal how the brain produces richly detailed visual representations so quickly.

Anne Trafton | MIT News Office • mit
March 4, 2020 ~10 min

Demystifying the world of deep networks

Researchers discover that no magic is required to explain why deep networks generalize despite going against statistical intuition.

Kris Brewer | Center for Brains, Minds and Machines • mit
Feb. 28, 2020 ~4 min

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